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Postponement Strategies in Supply Chain Management

:	T. C. Edwin Cheng, Jian Li, C. L. Johnny Wan, Shouyang Wang
:	Postponement Strategies in Supply Chain Management
:	Springer-Verlag
:	9781441958372
:	1
:	CHF 85.90
:

:	Allgemeines, Lexika
:	English

:	166
:	Wasserzeichen/DRM
:	PC/MAC/eReader/Tablet
:	PDF

Postponement strategy is one of the major supply chain management (SCM) pr- tices that has a discernible impact on rms' competitive advantage and organi- tional performance. Postponement is a mass customization strategy that captures the advantages of both mass production and mass customization. Recent research studies have identi ed four common postponement strategies, namely pull, logistics, form and price postponement. The former three postponement strategies are linked to production and manufacturing, while the last one is a pure pricing strategy. They aim at balancing the costs and bene ts of mass production and mass customization. Practical examples of postponement can be found in the high-tech industry, food industry and other industries that require high differentiation. However, empirical studies have found that postponement may not be an evident SCM practice compared to the other practices. In addition, postponement has both positive and negative impacts on a supply chain. The advantages include following the JIT principles, reducing end-product inventory, making forecasting easier and pooling risk. The high cost of designing and manufacturing generic components is the main drawback of postponement. Thus, the evaluation of postponement strategy is an important research issue and there have been many qualitative and quantitative models for analyzing postponement under different scenarios.

	Abstract	6
	Preface	8
	Contents	12
	List of Figures	16
	List of Tables	18
	1 Introduction	20
	1.1 From Product Variety to Postponement	20
	1.1.1 Product Variety	20
	1.1.2 Mass Customization	21
	1.1.3 Postponement Strategy	22
	1.2 Classification of Postponement	22
	1.2.1 Pull Postponement	23
	1.2.2 Logistics Postponement	25
	1.2.3 Form Postponement	26
	1.2.4 Price Postponement	27
	1.2.5 Implications	27
	1.2.6 Advantages and Disadvantages of Postponement	28
	1.2.7 Prerequisites for Postponement Strategy Development	29
	1.3 Cost Models for Analyzing Postponement Strategies	30
	1.3.1 Stochastic Models	30
	1.3.2 Heuristic Models	31
	1.3.3 Descriptive Models	32
	1.3.4 Performance Measures	33
	1.4 A Literature Review for Model Development	33
	1.4.1 EOQ and EPQ Models	34
	1.4.2 Lot Size-Reorder Point Model	35
	1.4.3 Markov Chain	35
	1.5 Concluding Remarks	36
	2 Analysis of Pull Postponement by EOQ-based Models	37
	2.1 Postponement Strategy for Ordinary (Imperishable) Items	37
	2.1.1 Proposed Model and Assumptions	37
	2.1.2 Case 1: Same Backorder Cost	40
	2.1.3 Case 2: Different Backorder Costs	44
	2.1.4 A Numerical Example	48
	2.2 Postponement Strategy for Perishable Items	50
	2.2.1 Notation and Assumptions	51
	2.2.2 Model Formulation	52
	2.2.3 The Postponement and Independent Systems	56
	2.2.4 Numerical Examples	57
	2.3 Concluding Remarks	59
	3 Analysis of Postponement Strategy by EPQ-based Models	60
	3.1 Analysis of Postponement Strategy by an EPQ-based Model without Stockout	60
	3.1.1 Proposed Model and Assumptions	60
	3.1.2 2 Machines for 2 End-Products	63
	3.1.3 n Machines for n End-Products	73
	3.2 Analysis of Postponement Strategy by an EPQ-based Model with Planned Backorders	79
	3.2.1 Proposed Model and Assumptions	80
	3.2.2 Demands Are Met Continuously	82
	3.2.3 Demands Are Met After Production Is Complete	88
	3.3 Concluding Remarks	95
	4 Evaluation of a Postponement Systemwith an (r,q) Policy	97
	4.1 The Proposed Models and Assumptions	97
	4.2 System Dynamics for a Non-postponement System	99
	4.3 The Algorithm for Finding a Near Optimal Total Average Cost of an (r,q) Policy	100
	4.3.1 The Markov Chain Development	100
	4.3.2 The Algorithm for Finding a Near Optimal Total Average Cost	115
	4.4 System Dynamics for a Postponement System	118
	4.5 Average Cost Comparison of the Two Systems When L=0	119
	4.6 Average Cost Comparison of the Two Systems When L1	120
	4.6.1 An Overview of the Simulation Results	120
	4.6.2 Impacts of Parameters on Average Cost	122
	4.7 Concluding Remarks	123
	5 Simulation of a Two-End-Product Postponement System	125
	5.1 Proposed Model and Assumptions	126
	5.1.1 Notation	127
	5.1.2 Model Assumptions	127
	5.2 Methodology	128
	5.2.1 System Dynamics	128
	5.2.2 The Simulation Model	130
	5.2.3 Customer Demand Distribution	130
	5.2.4 Order Quantity and Reorder Point	131
	5.2.5 Summary of Parameters	131
	5.2.6 Initial Conditions	131
	5.3 Simulation Results for Non-cost Parameters	133
	5.3.1 Uniform Distribution	133
	5.3.2 Poisson Distribution	134
	5.3.3 Normal Distribution I	135
	5.3.4 Normal Distribution II	136
	5.4 Simulation Results for Cost Parameters	137
	5.5 Concluding Remarks	139
	6 Application of Postponement: Examples from Industry	140
	6.1 A Case Study from Hong Kong	140
	6.1.1 An Overview of the Company	141
	6.1.2 Implementation of Postponement	141
	6.1.3 Benefits of Using Postponement	142
	6.1.4 Implications	143
	6.2 The Case of Taiwanese Information Technology Industry	144
	6.2.1 The Hypothesis	144
	6.2.2 Methodology	145
	6.2.3 Results	146
	6.2.4 Implications	146
	6.3 Concluding Remarks	147
	7 Conclusions, Implications and Future Research Directions	148
	7.1 Conclusions	148
	7.2 Implications and Further Research Directions	149
	A Simulation Results (Uniform Distribution)	152
	B Simulation Results (Poisson Distribution)	156
	C Simulation Results (Normal Distribution I)	162
	D Simulation Results (Normal Distribution II)	166
	E Simulation Results for Cost Analysis	170
	References	172
	About the Authors	178
	Index	180